DEVELOPMENT OF FRONT END AND STATISTICAL MODEL FOR A HINDI SPEECH RECOGNIZER : A PRACTICAL APPROACH

Satish Kumar & Prof. Jai Prakash

ABSTRACT

This paper describes the different approaches for the development of front end and statistical model for Hindi Speech Recognizer. The front end includes the preprocessing of speech signal such as capturing of raw speech signal, its digitization and converting it into Mel Frequency Cepstral Co-efficient(MFCC) vectors followed by Vector Quantization(VQ) step for the purpose of reducing the quantity of data which is to be used for the development of statistical model such as Hidden Markov Model (HMM) designated by parameter λ = [ A, B, π ]. A tutorial approach has been adopted in this paper for finding out various parameters such as MFCC vectors and Transition matrix (A), Observation symbol probability distribution (B), Initial state distribution (π), Number of distinct observation symbols (M), Number of states (N) which are to be used for development of a statistical model at various stages of the development of this paper